You love building web apps with Python, and HTMX got you excited about the hypermedia approach -- let the server drive the HTML, skip the JavaScript build step, keep things simple. But then you hit that last 10%: You need Alpine.js for interactivity, your state gets out of sync, and suddenly you're juggling two unrelated libraries that weren't designed to work together.

What if there was a single 11-kilobyte framework that gave you everything HTMX and Alpine do, and more, with real-time updates, multiplayer collaboration out of the box, and performance so fast you're actually bottlenecked by the monitor's refresh rate? That's Datastar.

On this episode, I sit down with its creator Delaney Gillilan, core maintainer Ben Croker, and Datastar convert Chris May to explore how this backend-driven, server-sent-events-first framework is changing the way full-stack developers think about the modern web.

Episode sponsors

Sentry Error Monitoring, Code talkpython26
Command Book
Talk Python Courses

Links from the show

Download audio: https://talkpython.fm/episodes/download/537/datastar-modern-web-dev-simplified.mp3

For most of my career, I’ve been a proud Windows developer.

I build Windows apps. I run Windows as my primary dev machine. It’s been home.

But recently? That’s been challenged — not by another OS, but by AI.

Developer workflow is changing fast. Agents, containers, automation — they’re reshaping how we build software. So let’s rewind a little, talk about why Windows, and then look at what’s shifting under our feet.

---

Why Windows?

There are really two answers.

1️⃣ The Tools — Visual Studio

For years, Visual Studio was untouchable.

While others were sprinkling Console.WriteLine or scanning logs, Windows developers had:

• Step-through debugging
• Deep variable inspection (even the gnarly ones)
• Conditional breakpoints
• Edit-and-Continue (Hot Reload)
• Integrated build, test, and profiling

It wasn’t just an editor — it was a command center.

And honestly? It still is when I need serious debugging power.

---

2️⃣ The Stack — XAML and Beyond

The second reason: the Windows UI stack.

Since the early days of WPF and Silverlight, I’ve loved XAML. Declarative UI. Clean separation. Powerful binding.

As the platform evolved — WPF → Win8 → UWP → WinUI — XAML stayed central.

Now with the Uno Platform, that same XAML lets me target:

• Windows (WinUI & Desktop)
• macOS
• Linux
• Android
• iOS
• Web (WASM)

One UI definition. Everywhere.

Pair that with MVVM and you get a clean, structured architecture. Add MVUX from Uno and you get a reactive, modern state management model.

It’s simple. Powerful. Productive.

---

So What Changed?

For the first time in my career…

I’m not building for WinUI first.

That used to be my workflow:

1. Build and debug on WinUI
2. Then test on other Uno targets

But when Uno recommended the Skia backend, things shifted.

Now I default to the Desktop target (Windows, macOS, Linux). Why?

• Faster build and launch
• Clearer XAML errors
• Better day-to-day debugging flow

Ironically, building for Windows became less about WinUI itself.

And that shift matters — because an even bigger shift was coming.

---

Enter AI Agents + App MCP

This is where things really changed.

With AI agents — whether it’s Claude, Copilot, or Codex — and the Uno Platform App MCP:

An agent can:

• Build the app
• Run it
• Take screenshots
• Verify changes
• Repeat across Desktop, Web, and Mobile

I can now assign an entire issue to an agent.

It will:

1. Plan the solution
2. Implement it
3. Run the app
4. Validate the outcome

Across multiple platforms.

That’s not autocomplete.

That’s delegation.

---

Containers, YOLO Mode, and Dev Isolation

The next evolution?

Running agents in YOLO mode inside a dev container.

I’m using:

• A Linux container
• Running on WSL
• Connected via VS Code

The dev container stays locked down:

• Limited GitHub access
• Controlled environment
• Isolation from the host

I can still launch desktop and web targets for debugging — but the agent operates in a contained sandbox.

It’s structured chaos.

---

Where That Leaves Me

So here I am.

Still using Windows.
Still building for Windows.
But not the way I used to.

Visual Studio is now my “serious debugging” tool.

Day-to-day development?
Often handled by an AI agent — inside a container — through whatever console I happen to be using.

That’s a massive shift.

---

The Bigger Picture

AI isn’t just speeding up development.

It’s redefining the workflow.

Agents. Sub-agents. Teams of agents.
Parallelizing feature work.
Burning down backlogs.
Testing across platforms automatically.

The tooling and models are evolving monthly.

Six months from now? The workflow may look completely different again.

One thing is clear:

Being a developer today isn’t just about writing code.

It’s about orchestrating intelligence.

The post I used to be a Windows Developer….. and I still am (sort of) appeared first on Nick's .NET Travels.

Interesting Engineering reports: A newly uncovered flaw in Discord’s age verification rollout has added fresh pressure to the company’s 2026 compliance plans. Security researchers recently found that frontend components tied to identity vendor Persona were accessible on the open web, prompting debate over how securely the platform handles sensitive age checks. The discovery surfaced on...

Source

After releasing AI Context Kit, I learned about two emerging standards that are becoming essential for durable AI collaboration: AGENTS.md for project operation and Agent Skills for workflow authority. In this post, I explain why I transitioned to these standards, how I executed the migration using the Codex macOS App, and what the new architecture looks like.

BONUS: From Combat Pilot to Scrum Master - How Military Leadership Transforms Agile Teams

In this bonus episode, we explore a fascinating career transition with Nate Amidon, a former Air Force combat pilot who now helps software teams embed military-grade leadership principles into their Agile practices. Nate shares how the high-stakes discipline of aviation translates directly into building high-performing development teams, and why veterans make exceptional Scrum Masters.

The Brief-Execute-Debrief Cycle: Aviation Meets Agile

"We would mission brief in the morning and make sure everyone was on the same page. Then we problem-solved our way through the day, debriefed after, and did it again. When I learned about what Agile was, I realized it's the exact same thing."

 

Nate's transition from flying C-17 cargo planes to working with Agile teams wasn't as jarring as you might expect. Flying missions that lasted 2-3 weeks with a crew of 5-7 people taught him the fundamentals of iterative work: daily alignment, continuous problem-solving, and regular reflection. The brief-execute-debrief cycle that every military pilot learns mirrors the sprint cadence that Agile teams follow. Time-boxing wasn't new to him either—when you're flying, you only have so much fuel, so deadlines aren't arbitrary constraints but physical realities that demand disciplined execution.

In this episode with Christian Boucousis, we also discuss the brief-execute-debrief cycle in detail. 

In this segment, we also refer to Cynefin, and the classification of complexity. 

Alignment: The Real Purpose Behind Ceremonies

"It's really important to make sure everyone understands why you're doing what you're doing. We don't brief, execute, debrief just because—we do it because we know that getting everybody on the same page is really important."

 

One of the most valuable insights Nate brings to his work with software teams is the understanding that Agile ceremonies aren't bureaucratic checkboxes—they're alignment mechanisms. The purpose of sprint planning, daily stand-ups, and retrospectives is to ensure everyone knows the mission and can adapt when circumstances change. Interestingly, Nate notes that as teams become more high-performing, briefings get shorter and more succinct. The discipline remains, but the overhead decreases as shared context grows.

The Art of Knowing When to Interrupt

"There are times when you absolutely should not interrupt an engineer. Every shoulder tap is a 15-minute reset for them to get back into the game. But there are also times when you absolutely should shoulder tap them."

 

High-performing teams understand the delicate balance between deep work and necessary communication. Nate shares an aviation analogy: when loadmasters are loading complex cargo like tanks and helicopters, interrupting them with irrelevant updates would be counterproductive. But if you discover that cargo shouldn't be on the plane, that's absolutely worth the interruption. This judgment—knowing what matters enough to break flow—is something veterans develop through high-stakes experience. Building this awareness across a software team requires:

 

• Understanding what everyone is working on

• Knowing the bigger picture of the mission

• Creating psychological safety so people feel comfortable speaking up

• Developing shared context through daily stand-ups and retrospectives

Why Veterans Make Exceptional Scrum Masters

"I don't understand why every junior officer getting out of the military doesn't just get automatically hired as a Scrum Master. If you were to say what we want a Scrum Master to do, and what a junior military officer does—it's line for line."

 

Nate's company, Form100 Consulting, specifically hires former military officers and senior NCOs for Agile roles, often bringing them on without tech experience. The results consistently exceed expectations because veterans bring foundational leadership skills that are difficult to develop elsewhere: showing up on time, doing what you say you'll do, taking care of team members, seeing the forest through the trees. These intangible qualities—combined with the ability to stay calm, listen actively, and maintain integrity under pressure—make for exceptional servant leaders in the software development space.

The Onboarding Framework for Veterans

"When somebody joins, we have assigned everybody a wingman—a dedicated person that they check in with regularly to bounce ideas off, to ask questions."

 

Form100's approach to transitioning veterans into tech demonstrates the same principles they advocate for Agile teams. They screen carefully for the right personality fit, provide dedicated internal training on Agile methodologies and program management, and pair every new hire with a wingman. This military unit culture helps bridge the gap between active duty service and the private sector, addressing one of the biggest challenges: the expectation gap around leadership standards that exists between military and civilian organizations.

Extreme Ownership: Beyond Process Management

"To be a good Scrum Master, you have to take ownership of the team's execution. If the product requirements aren't good, it's a Scrum Master's job to help. If QA is the problem, take ownership. You should be the vessel and ownership of the entire process of value delivery."

 

One of Nate's core philosophies comes from Jocko Willink's Extreme Ownership. Too many Scrum Masters limit themselves to being "process people" who set meetings and run ceremonies. True servant leadership means owning everything that affects the team's ability to deliver value—even things technically outside your job description. When retrospectives devolve into listing external factors beyond the team's control, the extreme ownership mindset reframes the conversation: "Did we give the stakeholder the right information? Did they make a great decision based on bad information we provided?" This shift from blame to ownership drives genuine continuous improvement.

Building Feedback Loops in Complex Environments

"In the military, we talk about the OODA loop. Everything gets tighter, we get better—that's why we do the debrief."

 

Understanding whether you're operating in a complicated or complex domain (referencing the Cynefin framework) determines how tight your feedback loops need to be. In complex environments—where most software development lives—feedback loops aren't just for reacting to what happened; they're for probing and understanding what's changing. Sprint goals become essential because without knowing where you're headed, you can't detect when circumstances have shifted. The product owner role becomes critical as the voice connecting business priorities to team execution, ensuring the mission stays current even when priorities change mid-sprint.

Recommended Resources

Nate recommends the following books: 

• Team of Teams by General McChrystal

• Extreme Ownership by Jocko Willink

 

About Nate Amidon

 

Nate is a former Air Force combat pilot and founder of Form100 Consulting. He helps software teams embed leadership at the ground level, translating military principles into Agile practices. With a focus on alignment, accountability, and execution, Nate empowers organizations to lead from within and deliver real results in a dynamic tech landscape.

 

You can link with Nate Amidon on LinkedIn and learn more at Form100 Consulting.

Download audio: https://traffic.libsyn.com/secure/scrummastertoolbox/20260221_Nate_Amidon_BONUS.mp3?dest-id=246429

I’m Matt Burns, Director of Editorial at Insight Media Group. Each week, I round up the most important AI developments, explaining what they mean for people and organizations putting this technology to work. The thesis is simple: workers who learn to use AI will define the next era of their industries, and this newsletter is here to help you be one of them.

---

This is a big week for Roadmap.sh: our site launched the Claude Code roadmap to help support the untold number of users jumping on the popular platform. It’s a comprehensive guide covering everything from vibe coding and agentic loops to MCP servers, plugins, hooks, and subagents. This is the place to start with Claude Code and AI-assisted coding, even if you’re already vibe coding elsewhere. It will help anyone jump from casual prompting to real agentic workflows. We’re proud to have it out there, and we think it speaks to how developer skillsets are being rewritten in real time, and the platforms mapping the new world are the ones who’ll own it.

This shift showed up clearly in this Towards Data Science interview with Stephanie Kirmer, a machine learning engineer with almost a decade in the field. She describes how LLMs have reshaped her daily work from using code assistants to bounce ideas off, critique approaches, and handle the grunt work around unit tests. She’s also candid about their limits, noting that the real value still comes from experience applied to unusual problems. Her take on the broader AI economy is worth sitting with, too. She thinks we’re in a bubble, not because the tech isn’t useful, but because the investment expectations are widely out of proportion. If the industry were willing to accept good returns on moderate investment rather than demand immense returns on a gigantic investment, she says, the current AI world could be sustainable.

Productivity Gains or AI Workslop?

AI tools are objectively improving, but are organizations actually becoming more productive? Fortune reported a study of thousands of C-suite executives who have yet to see AI produce a productivity boom. The executives surveyed invested heavily in AI but are struggling to translate that into measurable output gains. This is reminiscent of past technology shifts, first with PCs, and then with the internet and mobile.

Another study released this week points to AI training as the missing link. According to a major CEPR study across 12,000 European firms, the organizations that train their people to use AI are the organizations that benefit from it. The study notes that AI adoption increases labor productivity by 4% on average, with no evidence of short-run employment reductions. Every additional 1% of investment in workforce training amplified AI’s productivity effect by nearly 6%.

In short, the orgs that just buy AI licenses and expect magic are the orgs that are disappointed. Organizations need to invest both in AI and in employee upskilling.

AI Gets Cheaper and More Capable

Anthropic this week released Claude Sonnet 4.6, which promises near-Opus-level performance at significantly lower pricing. It even beats Anthropic’s Opus model on several tasks, and according to some benchmarks, it matches or outperforms Google’s Gemini 3 Pro and OpenAI’s GPT-5.2 in several categories. It’s a major upgrade to a mid-tier option.

This news should help push AI adoption among organizations on the fence by offering near-peak performance at a much lower cost.

On the other side, OpenAI launched Codex Spark, a model built for raw speed, capable of 1,000 tokens per second. It’s designed for rapid prototyping and real-time collaboration, complementing the heavier Codex model for long-running agentic tasks.

Google got in on the fun, too, releasing Gemini 3.1 Pro. While this is still in preview, early benchmarks show that it’s currently far better at solving complex problems than Google’s previous mainstream model. According to Google, the ‘core intelligence’ of Gemini 3.1 Pro comes directly from the Deep Think model, which explains why 3.1 Pro performs so well on reasoning benchmarks.

The pattern is clear: performance rises, prices fall, and the barrier to entry for orgs adopting AI keeps shrinking. If cost were an excuse for waiting, that excuse is shrinking with each new release.

OpenClaw: Still Fun, Still messy, and Now Headless

OpenClaw continues to be one of the most fascinating open source projects in the AI space. If you haven’t been following it, OpenClaw is, in short, a platform that runs Claude Code autonomously to be a user’s personal AI assistant. Buy a Mac Mini, install OpenClaw, and let it run your (or its) life. Eivind Kjosbakken published this OpenClaw walkthrough on TDS, and it’s a great practical guide for personalizing its behavior with skills and connecting it to Slack, GitHub, and Gmail. He reports massive efficiency gains within a week. Projects like this reinforce that the era of the AI assistant isn’t just a pipedream; it’s already becoming a reality.

The project took a turn on Sunday, though. OpenClaw founder Peter Steinberger announced he’s joining OpenAI to work on next-gen personal agents. The good news is OpenClaw is moving to a foundation, and Steinberger says it will remain open source. The less good news involves security. The New Stack reported that Snyk found over 7% of skills on ClawHub, the OpenClaw marketplace, contain flaws that expose sensitive credentials. Meanwhile, Anthropic caused a panic and later reaffirmed its policies, saying users can still use Claude accounts to run OpenClaw and similar tools.

The New Stacks’s Frederic Lardinois interviewed the founder of NanoClaw, a lightweight alternative to OpenClaw. Gavriel Cohen built the ClawBot alternative in a weekend after learning about security flaws in the popular agentic framework. NanoClaw launched on GitHub in late January and now has just under 10,000 stars. The core principle is radical minimalism: about a few hundred lines of actual code, a handful of dependencies, and each agent running inside its own container.

 

India is Betting Big on AI

India this week hosted a massive AI event. At the India AI Impact Summit, Replit CEO Amjad Masad put it bluntly: “Two kids in India can now compete with Salesforce.” Also at the event, Adani Group pledged $100 billion towards building AI-ready data centers in India by 2035, partnering with Google, Microsoft, and Flipkart to build what they say will be the world’s largest integrated data center platform.

And if you needed a kicker for how seriously the world’s biggest AI players are taking India, OpenAI’s Sam Altman and Anthropic’s Dario Amodei both showed up on stage at the summit alongside PM Modi. But they seemingly refused to hold hands for a traditional unity pose. They raised fists instead, standing right next to each other. It’s a small moment that captures an enormous truth: The rivalry between OpenAI and Anthropic is real and the stakes are global.

AI’s Biggest Funding Yet

OpenAI and Anthropic are both raising capital at astronomical levels. According to Bloomberg, OpenAI’s latest round is on track to exceed $100 billion, with Amazon expected to invest up to $50 billion, Softbank with up to $30 billion and Nvidia coming in around $20 billion. The company’s valuation could exceed $850 billion. For context, last week, Anthropic closed a $30 billion round at a $380 billion valuation, with annualized revenue hitting $14 billion. Both companies are reportedly preparing for a potential IPO.

There’s more. Fei-Fei Li’s World Labs raised $1 billion for its “spatial intelligence” approach to AI. Their pitch involves building models that understand 3D worlds rather than just flat text and images. Investors include AMD, Autodesk, and Nvidia. In the past, a billion-dollar raise would dominate the news cycle, but this one is barely making it above the fold, suggesting that the scale of investment is radically shifting.

Ads or no ads?

One more thread worth watching is how AI companies plan to actually make money. Perplexity announced that it’s ditching ads and going all-in on subscriptions. The reasoning is comforting: Users need to trust that every answer is the best possible answer, not one influenced by an advertiser. Valued at $20 billion with $200 million in ARR, Perplexity is betting that trust can be a moat.

Anthropic clearly feels the same way. The company ran a series of Super Bowl ads that are darkly funny, parodying what happens when your AI assistant starts serving ads mid-conversation. The tagline is sharp: “Ads are coming to AI. But not to Claude.” These spots were a direct shot at OpenAI, which recently began serving contextual ads at the bottom of ChatGPT conversations for free and Go-tier users.

As AI companies explore monetization, the lesson for users is the same: The underlying AI technology is being subsidized, competed over, and made more accessible (and cheaper) nearly every week.

The post Claude Code comes to Roadmap, OpenClaw loses its head, and AI workslop appeared first on The New Stack.